In this document we will examine the open dataset from Data.gov regarding NYC Shootings
This is a part of DTSA - 5301 “Data Science as a Field” coursework through the University of Colorado.
First we will read in the NYPD_Shooting_Incident_Data__Historic.csv from Data.gov. Then store this file locally for future use.
ny_shootings <- read_csv('https://data.cityofnewyork.us/api/views/833y-fsy8/rows.csv?accessType=DOWNLOAD', show_col_types = FALSE)
# if(!file.exists('data'))(dir.create('data'))
# write_csv(ny_shootings, "data/NYPD_Shooting_Incident_Data__Historic.csv")
# Comment out the above after the ititial read
# ny_shootings <- read_csv('data/NYPD_Shooting_Incident_Data__Historic.csv', show_col_types = FALSE)Number of rows: 29744
Number of columns: 21
Date range: 01/01/2006 to 12/31/2024
NYPD_Shooting_Incident_Data__Historicknitr::kable(slice_sample(ny_shootings, n = 5), caption = "NYC Shootings Data")| INCIDENT_KEY | OCCUR_DATE | OCCUR_TIME | BORO | LOC_OF_OCCUR_DESC | PRECINCT | JURISDICTION_CODE | LOC_CLASSFCTN_DESC | LOCATION_DESC | STATISTICAL_MURDER_FLAG | PERP_AGE_GROUP | PERP_SEX | PERP_RACE | VIC_AGE_GROUP | VIC_SEX | VIC_RACE | X_COORD_CD | Y_COORD_CD | Latitude | Longitude | Lon_Lat |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 25452383 | 11/22/2006 | 20:15:00 | BROOKLYN | NA | 73 | 0 | NA | NA | FALSE | NA | NA | NA | 45-64 | M | BLACK HISPANIC | 1007190.0 | 182916.0 | 40.66872 | -73.91731 | POINT (-73.91730820799995 40.668717396000034) |
| 233998219 | 09/23/2021 | 02:01:00 | BRONX | NA | 42 | 2 | NA | MULTI DWELL - PUBLIC HOUS | TRUE | NA | NA | NA | 18-24 | M | BLACK | 1011047.0 | 239814.0 | 40.82488 | -73.90318 | POINT (-73.90317795599998 40.82487717500004) |
| 23354103 | 07/05/2006 | 07:50:00 | BROOKLYN | NA | 81 | 0 | NA | GROCERY/BODEGA | FALSE | UNKNOWN | M | BLACK | 45-64 | M | BLACK | 1003998.8 | 187611.0 | 40.68161 | -73.92880 | POINT (-73.92879814299994 40.681611891000045) |
| 154350740 | 06/25/2016 | 20:29:00 | BRONX | NA | 52 | 0 | NA | NA | FALSE | 18-24 | M | WHITE HISPANIC | 25-44 | M | WHITE HISPANIC | 1013114.8 | 255492.0 | 40.86790 | -73.89564 | POINT (-73.89563923699995 40.867902049000065) |
| 83475189 | 03/02/2012 | 21:00:00 | STATEN ISLAND | NA | 122 | 0 | NA | PVT HOUSE | FALSE | NA | NA | NA | 25-44 | M | WHITE | 947940.4 | 159635.7 | 40.60477 | -74.13076 | POINT (-74.13076343299997 40.60477334400008) |
pander(summary(ny_shootings))| INCIDENT_KEY | OCCUR_DATE | OCCUR_TIME | BORO |
|---|---|---|---|
| Min. : 9953245 | Length:29744 | Length:29744 | Length:29744 |
| 1st Qu.: 67321140 | Class :character | Class1:hms | Class :character |
| Median :109291972 | Mode :character | Class2:difftime | Mode :character |
| Mean :133850951 | NA | Mode :numeric | NA |
| 3rd Qu.:214741917 | NA | NA | NA |
| Max. :299462478 | NA | NA | NA |
| NA | NA | NA | NA |
| LOC_OF_OCCUR_DESC | PRECINCT | JURISDICTION_CODE | LOC_CLASSFCTN_DESC |
|---|---|---|---|
| Length:29744 | Min. : 1.00 | Min. :0.0000 | Length:29744 |
| Class :character | 1st Qu.: 44.00 | 1st Qu.:0.0000 | Class :character |
| Mode :character | Median : 67.00 | Median :0.0000 | Mode :character |
| NA | Mean : 65.23 | Mean :0.3181 | NA |
| NA | 3rd Qu.: 81.00 | 3rd Qu.:0.0000 | NA |
| NA | Max. :123.00 | Max. :2.0000 | NA |
| NA | NA | NA’s :2 | NA |
| LOCATION_DESC | STATISTICAL_MURDER_FLAG | PERP_AGE_GROUP |
|---|---|---|
| Length:29744 | Mode :logical | Length:29744 |
| Class :character | FALSE:23979 | Class :character |
| Mode :character | TRUE :5765 | Mode :character |
| NA | NA | NA |
| NA | NA | NA |
| NA | NA | NA |
| NA | NA | NA |
| PERP_SEX | PERP_RACE | VIC_AGE_GROUP | VIC_SEX |
|---|---|---|---|
| Length:29744 | Length:29744 | Length:29744 | Length:29744 |
| Class :character | Class :character | Class :character | Class :character |
| Mode :character | Mode :character | Mode :character | Mode :character |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| NA | NA | NA | NA |
| VIC_RACE | X_COORD_CD | Y_COORD_CD | Latitude |
|---|---|---|---|
| Length:29744 | Min. : 914928 | Min. :125757 | Min. :40.51 |
| Class :character | 1st Qu.:1000094 | 1st Qu.:183042 | 1st Qu.:40.67 |
| Mode :character | Median :1007826 | Median :195506 | Median :40.70 |
| NA | Mean :1009442 | Mean :208722 | Mean :40.74 |
| NA | 3rd Qu.:1016739 | 3rd Qu.:239980 | 3rd Qu.:40.83 |
| NA | Max. :1066815 | Max. :271128 | Max. :40.91 |
| NA | NA | NA | NA’s :97 |
| Longitude | Lon_Lat |
|---|---|
| Min. :-74.25 | Length:29744 |
| 1st Qu.:-73.94 | Class :character |
| Median :-73.91 | Mode :character |
| Mean :-73.91 | NA |
| 3rd Qu.:-73.88 | NA |
| Max. :-73.70 | NA |
| NA’s :97 | NA |
In this next code cell, we will read in the population data for New York City by borough from a CSV file. This data will help us understand the population distribution across different boroughs, which can be useful for our analysis of shooting incidents.
boro_pop_raw <- read_csv('https://data.cityofnewyork.us/api/views/xywu-7bv9/rows.csv?accessType=DOWNLOAD', show_col_types = FALSE)
# write_csv(boro_pop_raw, "data/boro_pop_raw.csv")
#boro_pop_raw <- read_csv("data/boro_pop_raw.csv", show_col_types = FALSE)The data available by official sources for the population of New York City is in a wide format. We will need to tidy this data into a long format to make it easier to work with in our analysis.
Additionally, the data is only availble by decade. This is accurate enough for our purposes, but we will do some data assumptions to fill in the gaps for years between the decades. In this case we treat the decade population as the population at the midpoint of the decade, and will use linear interpolation to estimate the population for the years in between.
boro_pop <- boro_pop_raw %>%
select(Borough, `1950`, `1960`, `1970`, `1980`, `1990`, `2000`, `2010`, `2020`) %>%
mutate(Borough = toupper(Borough)) # Convert Borough to uppercase
boro_pop_long <- boro_pop %>%
pivot_longer(
cols = -Borough, # All columns except Borough
names_to = "Decade",
values_to = "Population"
) %>%
mutate(Decade = as.numeric(Decade)) %>% # Convert Decade to numeric
rename(Year = Decade) # Rename Decade to Year
boro_pop_interpolated <- boro_pop_long %>%
group_by(Borough) %>%
complete(Year = full_seq(c(Year, max(Year) + 5), 1)) %>% # Create rows for every year
arrange(Borough, Year) %>%
mutate(Population = approx(Year, Population, Year, rule = 2)$y) %>% # Linear interpolation
mutate(Population = lag(Population, n=5)) %>%
ungroup()
pander(summary(boro_pop_interpolated))| Borough | Year | Population |
|---|---|---|
| Length:456 | Min. :1950 | Min. : 191555 |
| Class :character | 1st Qu.:1969 | 1st Qu.:1380117 |
| Mode :character | Median :1988 | Median :1754239 |
| NA | Mean :1988 | Mean :2599322 |
| NA | 3rd Qu.:2006 | 3rd Qu.:2527506 |
| NA | Max. :2025 | Max. :8550971 |
| NA | NA | NA’s :30 |
# Print a sample of the interpolated data
knitr::kable(tail(boro_pop_interpolated), caption = "Boruough population interpolated from decade to years")| Borough | Year | Population |
|---|---|---|
| STATEN ISLAND | 2020 | 477942.5 |
| STATEN ISLAND | 2021 | 479785.0 |
| STATEN ISLAND | 2022 | 481627.5 |
| STATEN ISLAND | 2023 | 483470.0 |
| STATEN ISLAND | 2024 | 485312.5 |
| STATEN ISLAND | 2025 | 487155.0 |
In this section, we will analyze the number of murders per borough per year. This will help us understand the distribution of murders across different boroughs and how it changes over time.
First we will organize our data to group by boro and
year. Here is the summary of our new table which shows the
range and variance of the years and murder volume.
# Change "OCCUR_DATE" to an actual date datatype
ny_shootings <- ny_shootings %>%
mutate(OCCUR_DATE = as.Date(OCCUR_DATE, format="%m/%d/%Y"))
# Extract the year from OCCUR_DATE and create a new column
ny_shootings <- ny_shootings %>%
mutate(YEAR = lubridate::year(OCCUR_DATE))
# Group by borough and year and summarize only the murders
boro_deaths <- ny_shootings %>%
group_by(BORO, YEAR) %>%
summarize(murders = sum(STATISTICAL_MURDER_FLAG == TRUE, na.rm = TRUE), .groups = "drop") %>%
ungroup()
pander(summary(boro_deaths))| BORO | YEAR | murders |
|---|---|---|
| Length:95 | Min. :2006 | Min. : 0.00 |
| Class :character | 1st Qu.:2010 | 1st Qu.: 24.50 |
| Mode :character | Median :2015 | Median : 48.00 |
| NA | Mean :2015 | Mean : 60.68 |
| NA | 3rd Qu.:2020 | 3rd Qu.: 88.50 |
| NA | Max. :2024 | Max. :182.00 |
In order to get a more accurate understanding of the murder rate across the 5 boroughs, we will join the shooting data with the population data to calculate the number of shootings per capita. This will allow us to compare the murder rates across boroughs more fairly, accounting for differences in population size.
# Join with population to see a per capita View
boro_combined <- boro_deaths %>%
left_join(boro_pop_interpolated, by = c("BORO" = "Borough", "YEAR" = "Year"))
boro_combined <- boro_combined %>%
mutate(murders_per_capita = murders / Population)
pander(summary(boro_combined))| BORO | YEAR | murders | Population |
|---|---|---|---|
| Length:95 | Min. :2006 | Min. : 0.00 | Min. : 446228 |
| Class :character | 1st Qu.:2010 | 1st Qu.: 24.50 | 1st Qu.:1361502 |
| Mode :character | Median :2015 | Median : 48.00 | Median :1585873 |
| NA | Mean :2015 | Mean : 60.68 | Mean :1652030 |
| NA | 3rd Qu.:2020 | 3rd Qu.: 88.50 | 3rd Qu.:2286134 |
| NA | Max. :2024 | Max. :182.00 | Max. :2638898 |
| murders_per_capita |
|---|
| Min. :0.000e+00 |
| 1st Qu.:1.967e-05 |
| Median :2.779e-05 |
| Mean :3.523e-05 |
| 3rd Qu.:4.667e-05 |
| Max. :1.195e-04 |
knitr::kable(slice_sample(boro_combined, n = 5), caption = "Adds per capita view")| BORO | YEAR | murders | Population | murders_per_capita |
|---|---|---|---|---|
| STATEN ISLAND | 2013 | 4 | 463729.6 | 0.0000086 |
| MANHATTAN | 2018 | 15 | 1601595.4 | 0.0000094 |
| BRONX | 2021 | 170 | 1422116.0 | 0.0001195 |
| BRONX | 2017 | 52 | 1397444.0 | 0.0000372 |
| QUEENS | 2006 | 59 | 2231441.3 | 0.0000264 |
plot_ly(boro_combined, x = ~YEAR, y = ~murders_per_capita, color = ~BORO,
type = 'scatter', mode = 'lines') %>%
layout(title = "Murders Over Time by Borough",
xaxis = list(title = "Date"),
yaxis = list(title = "Murders per Capita"))We can see that for many Boroughs, the per capita shooting rates have been in decline since the data began being recorded. This suggests improvements in safety or changes in reporting over time. However, in the years of 2020 and 2021, there appears to be a significant increase in murder rates, which may be attributed to various social and economic factors during that period.
Pre-Pandemic Trends (2006-2019):
COVID-19 Pandemic Impact (2020-2021):
Post-Peak Trends (2022-2023):
In the next section we will look for patterns in the type of victims of shootings.
First we will examine the age and race distribution of shooting victims
ny_shootings %>%
count(VIC_AGE_GROUP) %>%
pander()| VIC_AGE_GROUP | n |
|---|---|
| 1022 | 1 |
| 18-24 | 10677 |
| 25-44 | 13563 |
| 45-64 | 2118 |
| 65+ | 236 |
| <18 | 3081 |
| UNKNOWN | 68 |
ny_shootings %>%
count(VIC_RACE) %>%
pander()| VIC_RACE | n |
|---|---|
| AMERICAN INDIAN/ALASKAN NATIVE | 13 |
| ASIAN / PACIFIC ISLANDER | 478 |
| BLACK | 20999 |
| BLACK HISPANIC | 2930 |
| UNKNOWN | 72 |
| WHITE | 741 |
| WHITE HISPANIC | 4511 |
There is one data point in the VIC_AGE_GROUP column that
appears to be an outlier or error. The value of 1022 does
not correspond to a valid age group and should be investigated or
removed from the analysis.
vic_demo <- ny_shootings %>%
filter(!VIC_AGE_GROUP %in% c("1022"))
vic_demo <- vic_demo %>%
group_by(VIC_AGE_GROUP, VIC_SEX, VIC_RACE) %>%
summarize(total_victims = n(), .groups = "drop")
# Create interactive plotly chart
plot_ly(vic_demo, x = ~VIC_AGE_GROUP, y = ~total_victims, color = ~VIC_RACE,
type = 'bar') %>%
layout(title = "Victim Demographics in NYC Shootings",
xaxis = list(title = "Age Group"),
yaxis = list(title = "Number of Victims"),
barmode = 'group', # This creates the dodged/grouped bars
legend = list(title = list(text = "Race")))Shootings seem to disproportionately affect certain age groups and racial demographics. However, we are not comparing these demographics to overall population ratios at this point. In order to surface any hypothesis here we will also need to find the relative popultation means and distributions for these demographics.
In the next Section we will build a logistic regression model to predict the likelihood of a murder based on various factors.
While this may produce insights, it is important to remember that we are only looking at the total count of shootings and looking for correlations where that shooting becomes a statistical murder.
Below we will build a generalized linear model to predict the likelihood of a murder based on severaly features in our dataset. GLM’s allow for modeling of binary outcomes and can provide insights into the factors that influence the probability of an event occurring.
# Create model specific data set
model_data <- ny_shootings %>%
mutate(murder_flag = as.numeric(STATISTICAL_MURDER_FLAG)) %>%
select(murder_flag, BORO, YEAR, PERP_AGE_GROUP, PERP_RACE, VIC_AGE_GROUP, VIC_RACE) %>%
na.omit()
model <- glm(murder_flag ~ BORO + YEAR + PERP_AGE_GROUP + PERP_RACE + VIC_AGE_GROUP + VIC_RACE,
data = model_data, family = "binomial")# Example: Highlight significant p-values in the model summary
tidy_model <- broom::tidy(model)
# Create color vector that handles NA values
p_value_colors <- ifelse(is.na(tidy_model$p.value), "black",
ifelse(tidy_model$p.value < 0.05, "red", "black"))
tidy_model %>%
knitr::kable(caption = "Model Summary", digits = 4) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(5, bold = TRUE, color = p_value_colors) %>%
footnote(general = "Red values indicate statistical significance (p < 0.05)")| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -4.1854 | 152.9892 | -0.0274 | 0.9782 |
| BOROBROOKLYN | -0.1170 | 0.0459 | -2.5489 | 0.0108 |
| BOROMANHATTAN | -0.1556 | 0.0573 | -2.7155 | 0.0066 |
| BOROQUEENS | -0.1452 | 0.0577 | -2.5170 | 0.0118 |
| BOROSTATEN ISLAND | -0.1958 | 0.1027 | -1.9077 | 0.0564 |
| YEAR | -0.0047 | 0.0034 | -1.4042 | 0.1603 |
| PERP_AGE_GROUP<18 | 0.4555 | 0.1090 | 4.1796 | 0.0000 |
| PERP_AGE_GROUP1020 | -11.8577 | 535.4112 | -0.0221 | 0.9823 |
| PERP_AGE_GROUP1028 | -11.6239 | 535.4112 | -0.0217 | 0.9827 |
| PERP_AGE_GROUP18-24 | 0.5635 | 0.0946 | 5.9593 | 0.0000 |
| PERP_AGE_GROUP2021 | -11.5474 | 535.4112 | -0.0216 | 0.9828 |
| PERP_AGE_GROUP224 | -11.8883 | 535.4112 | -0.0222 | 0.9823 |
| PERP_AGE_GROUP25-44 | 0.8123 | 0.0928 | 8.7548 | 0.0000 |
| PERP_AGE_GROUP45-64 | 1.1055 | 0.1161 | 9.5222 | 0.0000 |
| PERP_AGE_GROUP65+ | 1.1106 | 0.2737 | 4.0576 | 0.0000 |
| PERP_AGE_GROUP940 | -11.9076 | 535.4112 | -0.0222 | 0.9823 |
| PERP_AGE_GROUPUNKNOWN | -1.5967 | 0.1590 | -10.0394 | 0.0000 |
| PERP_RACEAMERICAN INDIAN/ALASKAN NATIVE | -12.6179 | 378.5670 | -0.0333 | 0.9734 |
| PERP_RACEASIAN / PACIFIC ISLANDER | 0.2028 | 0.1786 | 1.1350 | 0.2564 |
| PERP_RACEBLACK | -0.1065 | 0.0553 | -1.9275 | 0.0539 |
| PERP_RACEBLACK HISPANIC | -0.1406 | 0.0796 | -1.7660 | 0.0774 |
| PERP_RACEUNKNOWN | 0.1508 | 0.1436 | 1.0505 | 0.2935 |
| PERP_RACEWHITE | 0.3991 | 0.1430 | 2.7913 | 0.0052 |
| PERP_RACEWHITE HISPANIC | NA | NA | NA | NA |
| VIC_AGE_GROUP1022 | -11.8536 | 535.4112 | -0.0221 | 0.9823 |
| VIC_AGE_GROUP18-24 | 0.2283 | 0.0701 | 3.2558 | 0.0011 |
| VIC_AGE_GROUP25-44 | 0.3788 | 0.0692 | 5.4705 | 0.0000 |
| VIC_AGE_GROUP45-64 | 0.4370 | 0.0893 | 4.8911 | 0.0000 |
| VIC_AGE_GROUP65+ | 0.7089 | 0.1759 | 4.0306 | 0.0001 |
| VIC_AGE_GROUPUNKNOWN | 0.1701 | 0.3486 | 0.4880 | 0.6256 |
| VIC_RACEASIAN / PACIFIC ISLANDER | 11.9447 | 152.8362 | 0.0782 | 0.9377 |
| VIC_RACEBLACK | 11.7660 | 152.8361 | 0.0770 | 0.9386 |
| VIC_RACEBLACK HISPANIC | 11.5547 | 152.8361 | 0.0756 | 0.9397 |
| VIC_RACEUNKNOWN | 10.9219 | 152.8368 | 0.0715 | 0.9430 |
| VIC_RACEWHITE | 11.8120 | 152.8361 | 0.0773 | 0.9384 |
| VIC_RACEWHITE HISPANIC | 11.8169 | 152.8361 | 0.0773 | 0.9384 |
| Note: | ||||
| Red values indicate statistical significance (p < 0.05) |
In the above output, we can look for features that have a significant impact on the likelihood of a murder given a shooting happened.
Important note is to look for the coefficients and their statistical significance to understand which factors are most influential.
The features highlighted in red show statistical significance (p < 0.05), meaning:
For example: - If VIC_AGE_GROUP65+ has a positive
coefficient and p < 0.05, we can conclude that victims over
65 are significantly more likely to die from gunshot wounds
compared to the reference age group - If YEAR has a
negative coefficient and p < 0.05, it suggests that shootings
have become less lethal over time, controlling for other
factors
While statistical significance indicates a relationship exists, it does not tell us:
Example: If victim race shows significance, this likely reflects systemic inequalities in access to emergency medical care, not inherent biological differences.
Below we will use our predictive model to visualize the prediction probability organized buy a specific feature. While this has some interesting information, we must also recognize that this is only one feature of the data and there may be confounding variables to consider.
model_data <- model_data %>%
mutate(predicted_prob = predict(model, type = "response"))
model_data_filtered <- model_data %>%
filter(!VIC_AGE_GROUP %in% c("1020",
"1028",
"2021",
"224",
"940"))
# Create interactive plotly boxplot
plot_ly(model_data_filtered, y = ~predicted_prob, x = ~VIC_AGE_GROUP,
color = ~VIC_AGE_GROUP, type = "box") %>%
layout(title = "Predicted Probability of Murder by Victim Age Group",
xaxis = list(title = "Victim Age Group"),
yaxis = list(title = "Predicted Probability"),
showlegend = FALSE)In this visual we can see that as age increase, shootings are more leathal when the victim is older.
There are many opportunities for bias in this analysis, including data collection, feature selection, and model assumptions. It is important to further critically evaluate these factors to ensure that the conclusions drawn are valid and fair.
This analysis did not validate the methods of data collection or examine potential biases in how the data was gathered, which could affect the results.
The choice of features included in the model may show some correlation with the outcome but could be proxies for other underlying factors not included in the analysis.
Outliers or missing data in the data could disproportionately influence the model’s results, leading to misleading conclusions. A deeper examination of outliers and their impact on the analysis was not conducted.
The population data used for per capita calculations were estimates and may not accurately reflect the true population during the years analyzed, potentially skewing the results.
This analysis highlights disparities in gun violence between NYC neighborhoods and demonstrates how crisis events like the COVID-19 pandemic might exacerbate existing inequalities.
Session Info
pander(sessionInfo())R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: kableExtra(v.1.4.0), plotly(v.4.11.0), pander(v.0.6.6), details(v.0.4.0), lubridate(v.1.9.4), forcats(v.1.0.1), stringr(v.1.5.2), dplyr(v.1.1.4), purrr(v.1.1.0), readr(v.2.1.5), tidyr(v.1.3.1), tibble(v.3.3.0), ggplot2(v.4.0.0) and tidyverse(v.2.0.0)
loaded via a namespace (and not attached): sass(v.0.4.10), generics(v.0.1.4), xml2(v.1.4.1), stringi(v.1.8.7), hms(v.1.1.4), digest(v.0.6.37), magrittr(v.2.0.4), evaluate(v.1.0.5), grid(v.4.3.3), timechange(v.0.3.0), RColorBrewer(v.1.1-3), fastmap(v.1.2.0), jsonlite(v.2.0.0), backports(v.1.5.0), httr(v.1.4.7), crosstalk(v.1.2.2), viridisLite(v.0.4.2), scales(v.1.4.0), textshaping(v.1.0.4), lazyeval(v.0.2.2), jquerylib(v.0.1.4), cli(v.3.6.5), crayon(v.1.5.3), rlang(v.1.1.6), bit64(v.4.6.0-1), withr(v.3.0.2), cachem(v.1.1.0), yaml(v.2.3.10), parallel(v.4.3.3), tools(v.4.3.3), tzdb(v.0.5.0), broom(v.1.0.10), curl(v.7.0.0), vctrs(v.0.6.5), R6(v.2.6.1), png(v.0.1-8), lifecycle(v.1.0.4), bit(v.4.6.0), htmlwidgets(v.1.6.4), vroom(v.1.6.6), pkgconfig(v.2.0.3), desc(v.1.4.3), clipr(v.0.8.0), pillar(v.1.11.1), bslib(v.0.9.0), gtable(v.0.3.6), glue(v.1.8.0), data.table(v.1.17.8), Rcpp(v.1.1.0), systemfonts(v.1.3.1), xfun(v.0.54), tidyselect(v.1.2.1), rstudioapi(v.0.17.1), knitr(v.1.50), farver(v.2.1.2), htmltools(v.0.5.8.1), svglite(v.2.2.2), rmarkdown(v.2.30), compiler(v.4.3.3) and S7(v.0.2.0)